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Abstract Background A spinal cord injury (SCI) can result in severe impairment and fatality as well as significant motor and sensory abnormalities. Exosomes produced from IPSCs have demonstrated therapeutic promise for accelerating spinal cord injury recovery, according to a recent study. Objective This study aims to develop engineered IPSCs-derived exosomes (iPSCs-Exo) capable of targeting and supporting neurons, and to assess their therapeutic potential in accelerating recovery from spinal cord injury (SCI). Methods iPSCs-Exo were characterized using Transmission Electron Microscopy (TEM), Nanoparticle Tracking Analysis (NTA), and western blot. To enhance neuronal targeting, iPSCs-Exo were bioengineered, and their uptake by neurons was visualized using PKH26 labeling and fluorescence microscopy. In vitro, the anti-inflammatory effects of miRNA-loaded engineered iPSCs-Exo were evaluated by exposing neurons to LPS and IFN-γ. In vivo, biodistribution of engineered iPSC-Exo was monitored using a vivo imaging system. The therapeutic efficacy of miRNA-loaded engineered iPSC-Exo in a SCI mouse model was assessed by Basso Mouse Scale (BMS) scores, H&E, and Luxol Fast Blue (LFB) staining. Results The results showed that engineered iPSC-Exo loaded with miRNA promoted the spinal cord injure recovery. Thorough safety assessments using H&E staining on major organs revealed no evidence of systemic toxicity, with normal organ histology and biochemistry profiles following engineered iPSC-Exo administration. Conclusion These results suggest that modified iPSC-derived exosomes loaded with miRNA have great potential as a cutting-edge therapeutic approach to improve spinal cord injury recovery. The observed negligible systemic toxicity further underscores their potential safety and efficacy in clinical applications.

Abstract Relaxor ferroelectrics are highly desired for pulse-power dielectric capacitors, however it has become a bottleneck that substantial enhancements of energy density generally sacrifice energy efficiency under superhigh fields. Here, we demonstrate a novel concept of highly polarizable concentrated dipole glass in delicately-designed high-entropy (Bi1/3Ba1/3Na1/3)(Fe2/9Ti5/9Nb2/9)O3 ceramic achieved via substitution of multiple heterovalent ferroelectric-active principal cation species on equivalent lattice sites. The atomic-scaled polar heterogeneity of dipoles with different polar vectors between adjacent unit cells enables diffuse reorientation process but disables appreciable growth with electric fields. These unique features cause superior recoverable energy density of ~15.9 J cm−3 and efficiency of ~93.3% in bulk ceramics. We also extend the highly polarizable concentrated dipole glass to the prototype multilayer ceramic capacitor, which exhibits record-breaking recoverable energy density of ~26.3 J cm−3 and efficiency of ~92.4% with excellent temperature and cycle stability. This research presents a distinctive approach for designing high-performance energy-storage dielectric capacitors.

Background/aims The study aims to evaluate choroidal vascular changes in younger patients with myopia using ultrawidefield swept-source optical coherence tomography angiography (SS-OCTA).Methods Overall, 724 eyes of 362 participants (177 males, 185 females, age: 32.43±6.20 years) underwent SS-OCTA imaging (24×20 mm). The eyes were divided into normal, low myopia (LM), moderate myopia (MM), high myopia and superhigh myopia groups according to the spherical equivalent refraction (SER). Changes in choroidal vascular thickness (ChVT) and density (ChVD) in nine grids of the fundus were analysed using the latest version of the built-in analysis software.Results Axial length (AL) showed a negative correlation with SER (r=0.822, p=0.000); ChVT and ChVD showed a negative correlation with AL (p≤0.001) in all nine grids. In group analysis, compared with normal eyes, myopia affects ChVT earlier than ChVD, as observed in the LM and MM groups, respectively. The decrease in ChVT was most evident in the macular grid (β = −34.20, p=0.000), whereas the decrease in ChVD was most evident in the optic disc grid (β = −2.19, p=0.001).Conclusion Myopia has a significant impact on choroidal vascular structure, resulting in spatiotemporal differences. Using SS-OCTA with a new version of the built-in analysis software and a study with a larger sample cohort may aid in providing more authentic information on choroidal vascular changes in eyes with myopia.

Modeling long-range dependencies among features has become a consensus to improve the results of single image super-resolution (SISR), which stimulates interest in enlarging the kernel sizes in convolutional neural networks (CNNs). Although larger kernels definitely improve the network performance, network parameters and computational complexities are raised sharply as well. Hence, an optimization of setting the kernel sizes is required to improve the efficiency of the network. In this work, we study the influence of the positions of larger kernels on the network performance, and propose a scalable attention network (SCAN). In SCAN, we propose a depth-related attention block (DRAB) that consists of several multi-scale information enhancement blocks (MIEBs) and resizable-kernel attention blocks (RKABs). The RKAB dynamically adjusts the kernel size concerning the locations of the DRABs in the network. The resizable mechanism allows the network to extract more informative features in shallower layers with larger kernels and focus on useful information in deeper layers with smaller ones, which effectively improves the SR results. Extensive experiments demonstrate that the proposed SCAN outperforms other state-of-the-art lightweight SR methods. Our codes are available at https://github.com/ginsengf/SCAN.

Sulfide all-solid-state lithium batteries (SASSLBs) with a single-crystal nickel-rich layered oxide cathode (LiNixCoyMn1-x-yO2, x ​≥ ​0.8) are highly desirable for advanced power batteries owing to their excellent energy density and safety. Nevertheless, the cathode material's cracking issue and its severe interfacial problem with sulfide solid electrolytes have hindered the further development. This study proposes to employ surface modification engineering to produce B-NCM cathode materials coated with boride nanostructure stabilizer in situ by utilizing NCM encapsulated with residual lithium. This approach enhances the electrochemical performance of SASSLBs by effectively inhibiting electrochemical-mechanical degradation of the NCM cathode material on cycling and reducing deleterious side reactions with the solid sulfide electrolyte. The B-NCM/LPSCl/Gr SASSLBs demonstrate impressive cycling stability, retaining 84.19 ​% of its capacity after 500 cycles at 0.2 ​C, which represents a 30.13 ​% increase vs. NCM/LPSCl/Gr. It also exhibits a specific capacity of 170.4 mAh/g during its first discharge at 0.1 ​C. This work demonstrates an effective surface engineering strategy for enhancing capacity and cycle life, providing valuable insights into solving interfacial problems in SASSLBs.

TBM has been widely used in underground engineering and construction, but there is no precedent for the application of open TBM in the inclined shafts of coal mines, which brings new challenges to the support system. The distribution of the axial forces on anchors and the range of loosening of the surrounding rock are crucial considerations in tunnel support design. Existing methods for measuring the axial forces in anchors and determining the extent of loosening in the surrounding rock typically remain at the inspection level, lacking long-term and real-time monitoring capabilities. This paper presents a new self-sensing anchor with embedded optical fibers (made using an improved stirrer) and proposes an intelligent tunnel rock monitoring system. The paper also outlines a method for identifying loosening zones in surrounding rock based on monitoring data and theoretical analysis. Installing self-sensing anchors in the deep sections of the rock surrounding a tunnel provides three-dimensional, round-the-clock real-time monitoring of the axial forces acting on the anchors, using new technology and methods to recognize the deformation characteristics of loosening zones within the surrounding rock. This new self-sensing fiber optic anchor was first applied to an open TBM tunneling project in an inclined shaft in the Kekegai coal mine, and monitoring data indicate that self-sensing optical fiber anchors can accurately reflect stress patterns in real time. The axial force curve can be divided into four segments: the borehole area, the loosening zone, the stable zone, and the anchoring zone. Consequently, it accurately identifies the thickness of loosening zones at different positions within the tunnel’s surrounding rock. This information is compared and verified against results obtained from bolt dynamometers and borehole inspection. On this basis, an intelligent monitoring system was established to provide a basis for making engineering construction decisions, which makes tunnel construction smarter and helps technicians timely adjust TBM driving and support parameters.

Misusage of tetracycline (TC) antibiotics residue in animal food has posed a significant threat to human health. Therefore, there is an urgent need to develop highly sensitive and robust assays for detecting TC. In the current study, gold and platinum nanoparticles were deposited on carbon nanotubes (CNTs) through the superposition method (Au@Pt/CNTs-s) and one-pot method (Au@Pt/CNTs-o). Au@Pt/CNTs-s displayed higher enzyme-like activity than Au@Pt/CNTs-o, which were utilized for the development of sensitive magnetic immunoassays. Under the optimized conditions, the limits of detection (LODs) of magnetic immunoassays assisted by Au@Pt/CNTs-s and Au@Pt/CNTs-o against TCs could reach 0.74 ng/mL and 1.74 ng/m, respectively, which were improved 6-fold and 2.5-fold in comparison with conventional magnetic immunoassay. In addition, the measurement of TC-family antibiotics was implemented by this assay, and ascribed to the antibody used that could recognize TC, oxytetracycline, chlortetracycline, and doxycycline with high cross-reactivity. Furthermore, the method showed good accuracy (recoveries, 92.1–114.5% for milk; 88.6–92.4% for pork samples), which also were applied for determination of the targets in real samples. This study provides novel insights into the rapid detection of targets based on high-performance nanocatalysts.

Conventional spherical nucleic acid enzymes (SNAzymes), made with gold nanoparticle (AuNPs) cores and DNA shells, are widely applied in bioanalysis owing to their excellent physicochemical properties. Albeit important, the crowded catalytic units (such as G-quadruplex, G4) on the limited AuNPs surface inevitably influence their catalytic activities. Herin, a hybridization chain reaction (HCR) is employed as a means to expand the quantity and spaces of G4 enzymes for their catalytic ability enhancement. Through systematic investigations, we found that when an incomplete G4 sequence was linked at the sticky ends of the hairpins with split modes (3:1 and 2:2), this would significantly decrease the HCR hybridization capability due to increased steric hindrance. In contrast, the HCR hybridization capability was remarkably enhanced after the complete G4 sequence was directly modified at the non-sticky end of the hairpins, ascribed to the steric hindrance avoided. Accordingly, the improved SNAzymes using HCR were applied for the determination of AFB1 in food samples as a proof-of-concept, which exhibited outstanding performance (detection limit, 0.08 ng/mL). Importantly, our strategy provided a new insight for the catalytic activity improvement in SNAzymes using G4 as a signaling molecule.

Abstract 6G is believed to go beyond communication and provide integrated sensing and computing capabilities for a vision of Connected Intelligence with everything connected, everything sensed, and everything intelligent. Integrated sensing and communication will play a vital role for the fusion of physical and cyber worlds. The exploration of higher frequency bands, larger bandwidth, and more advanced large antenna technologies is paving the way towards the goal. In particular, the study of THz opens the possibility to have high resolution sensing and imaging capability on a communication mobile device. In this paper, we take a step along this direction and justify such possibility by building a THz sensing prototype with millimeter level imaging resolution while considering the physical aperture constraint of typical mobile device.

Objective This study was aimed to investigate the role of interleukin-1β (IL-1β) in cigarette smoke extract (CSE)-induced apoptosis in vascular smooth muscle cells and the underlying mechanism in a rat derived cell line. Methods Rat thoracic aortic smooth muscle cells (A7r5) were divided into six groups including control, CSE (model), CSE+ overexpression empty vector (OvExp-EV), CSE+IL-1β knockdown (KD), and CSE+ IL-1β knockdown empty vector (KD-EV). The mRNA expression levels of IL-1β and pregnancy-associated plasma protein A (PAPP-A) were detected by quantitative polymerase chain reaction (qPCR). The apoptosis of A7r5 cells was detected by flow cytometry. The expression levels of inflammatory mediators (TNFα, IL-6 and IL-8) and apoptotic proteins (Bax and Bcl-2) were determined by western blot. Results CSE induced significant apoptosis in vascular smooth muscle cells (P < 0.01) and elevated the mRNA levels of IL-1β and PAPP-A (P < 0.01). CSE administration increased protein expression of Bax, TNF-α, IL-6, and IL-8, with significantly reduced Bcl-2 expression (P < 0.01). IL-1β knockdown significantly decreased cell apoptosis via regulating the expression of these proteins (P < 0.05 or P < 0.01). Conclusion IL-1β is involved in CSE-induced PAPP-A expression and apoptosis in vascular smooth muscle cells, which might be considered as a target for preventing of cardiovascular diseases caused by cigarette smoking.

PurposeThe present study sought to evaluate the effects of pupil dilation on ocular parameter measurements and intraocular lens (IOL) power calculation using IOLMaster in highly myopic cataract patients.Materials and methodsA total of 233 eyes were included in this prospective study and assigned to four groups based on range of axial length (AL) as follows: group A:26–28 mm, group B:28–30 mm, group C:30–32 mm, and group D:32–36 mm. Flattest and steepest keratometry (K1 and K2), AL, anterior chamber depth (ACD), lens thickness (LT), and white-to-white (WtW) were determined using IOLMaster before and after administration of topical tropicamide. The corresponding IOL powers were calculated using Sanders–Retzlaff–Kraff/theoretical (SRK/T), Haigis, and Barrett Universal II formulas.ResultsVariations in AL, K1 and K2 following dilation were not significant (P > 0.05 in all groups). The results showed that ACD increased significantly after dilation (P = 0.000 in all groups), whereas LT decreased significantly after dilation (P = 0.000, 0.000, 0.001, and 0.003). Post-dilation WtW increased significantly in Group A, B, and C (P = 0.001, 0.001, and 0.025) but not in Group D. When IOL power was calculated as a discrete variable, significant differences were observed between pre- and post-dilation IOL power.ConclusionPupil dilation in cataract eyes with high myopia does not cause significant changes in AL and K. However, it significantly increases ACD as well as WtW values and significantly decreases the LT value. Surgeons should evaluate the effect of pupil dilation on IOL power prediction as the present findings show extreme cases. Notably, Barrett Universal II formula had the best concordance between different pupil conditions in long eyes.

Remote and real-time displacement measurements are crucial for a successful bridge health monitoring program. Researchers have attempted to monitor the deformation of bridges using remote sensing techniques such as an accelerometer when a static reference frame is not available. However, errors accumulate throughout the double-integration process, significantly reducing the reliability and accuracy of the displacement measurements. To obtain accurate reference-free bridge displacement measurements, this paper aims to develop a real-time computing algorithm based on hybrid sensor data fusion and implement the algorithm via smart sensing technology. By combining the accelerometer and strain gauge measurements in real time, the proposed algorithm can overcome the limitations of the existing methods (such as integration errors, sensor drifts, and environmental disturbances) and provide real-time pseud-static and dynamic displacement measurements of bridges under loads. A wireless sensor, SmartRock, containing multiple sensing units (i.e., triaxial accelerometer and strain gauges) and a Micro Controlling Unit (MCU) were utilized for remote data acquisition and signal processing. A remote sensing system (with SmartRocks, an antenna, an industrial computer, a Wi-Fi hotspot, etc.) was deployed, and a laboratory truss bridge experiment was conducted to demonstrate the implementation of the algorithm. The results show that the proposed algorithm can estimate a bridge displacement with sufficient accuracy, and the remote system is capable of the real-time monitoring of bridge deformations compared to using only one type of sensor. This research represents a significant advancement in the field of bridge displacement monitoring, offering a reliable and reference-free approach for remote and real-time measurements.

Abstract Background Eligibility criteria are the primary strategy for screening the target participants of a clinical trial. Automated classification of clinical trial eligibility criteria text by using machine learning methods improves recruitment efficiency to reduce the cost of clinical research. However, existing methods suffer from poor classification performance due to the complexity and imbalance of eligibility criteria text data. Methods An ensemble learning-based model with metric learning is proposed for eligibility criteria classification. The model integrates a set of pre-trained models including Bidirectional Encoder Representations from Transformers (BERT), A Robustly Optimized BERT Pretraining Approach (RoBERTa), XLNet, Pre-training Text Encoders as Discriminators Rather Than Generators (ELECTRA), and Enhanced Representation through Knowledge Integration (ERNIE). Focal Loss is used as a loss function to address the data imbalance problem. Metric learning is employed to train the embedding of each base model for feature distinguish. Soft Voting is applied to achieve final classification of the ensemble model. The dataset is from the standard evaluation task 3 of 5th China Health Information Processing Conference containing 38,341 eligibility criteria text in 44 categories. Results Our ensemble method had an accuracy of 0.8497, a precision of 0.8229, and a recall of 0.8216 on the dataset. The macro F1-score was 0.8169, outperforming state-of-the-art baseline methods by 0.84% improvement on average. In addition, the performance improvement had a p-value of 2.152e-07 with a standard t-test, indicating that our model achieved a significant improvement. Conclusions A model for classifying eligibility criteria text of clinical trials based on multi-model ensemble learning and metric learning was proposed. The experiments demonstrated that the classification performance was improved by our ensemble model significantly. In addition, metric learning was able to improve word embedding representation and the focal loss reduced the impact of data imbalance to model performance.

Contamination of deoxynivalenol (DON) in grains has attracted widespread concern. It is urgently needed to develop a highly sensitive and robust assay for DON high-throughput screening. Antibody against DON was assembled on the surface of immunomagnetic beads orientationally by the aid of Protein G. AuNPs were obtained under the scaffolding of poly(amidoamine) dendrimer (PAMAM). DON-horseradish peroxidase (HRP) was combined on the periphery of AuNPs/PAMAM by a covalent link to develop DON-HRP/AuNPs/PAMAM. Magnetic immunoassay based on DON-HRP/AuNPs/PAMAM was optimized and that based on DON-HRP/AuNPs and DON-HRP was adopted as comparison. The limits of detection (LODs) were 0.447 ng/mL, 0.127 ng/mL and 0.035 ng/mL for magnetic immunoassays based on DON-HRP, DON-HRP/Au and DON-HRP/Au/PAMAM, respectively. Magnetic immunoassay based on DON-HRP/AuNPs/PAMAM displayed higher specificity towards DON and was utilized to analyze grain samples. The recovery for the spiked DON in grain samples was 90.8–116.2% and the method presented a good correlation with UPLC/MS. It was found that the concentration of DON was in the range of ND-3.76 ng/mL. This method allows the integration of dendrimer–inorganic NPs with signal amplification properties for applications in food safety analysis.

Deep learning based on convolutional neural network (CNN) has been successfully applied to stereo matching, which has achieved greater improvement in speed and accuracy compared with traditional methods. However, existing CNN-based stereo matching frameworks frequently encounter two problems. First, the existing stereo matching network has a large number of parameters, which results in too long matching running time since excessive network width and excessive number of convolution kernels. Second, in some areas where reflection, refraction and fine structure may lead to ill-posed problems, the disparity estimation errors can be occurred. In this paper, we proposed a lightweight network, convolution attention residual network (CAR-Net), which can balance the real-time matching and matching accuracy for stereo matching. Besides, a multi-scale residual network called CBAM-ResNeXt, which combines attention, was proposed for features extraction. With an aim is to simplify the parameters of the network model by reducing the size of filters and to extract the semantic features such as categories and locations from the image through convolutional block attention module (CBAM). Here, the CBAM consists of channel attention module and spatial attention module, where the semantic information of the feature map can be fully maintained after the parameters were simplified. Moreover, we proposed a dimension-extended 3D-CBAM, which is connected to 3DCNN for cost aggregation. By combining these two sub-modules of attention, the network is guided to selectively focus on the foreground or background regions, so as to improve the disparity accuracy in the ill-posed regions. The experimental results showed that our proposed method generated high accuracy and optimized the velocity compared to the state-of-the-art benchmark on KITTI 2012, KITTI 2015 and Scene Flow.

The quantitative level of steroid hormones (SHs) in some body fluids have been accepted for clinical diagnosis, whereas their distribution in aqueous humor (AH) is unknown yet. Herein, a profiling study was conducted with a total of 171 AH and 107 plasma samples using liquid chromatography coupled with tandem mass spectrometry (LC MS/MS). For the first time, six kinds of SHs in AH were quantitatively estimated, and their abundances were ranked at cortisol (F), corticosterone (COR), androstenedione (A2), and 11-deoxycortisol (11DOC). The corresponding abundance of all SHs in AH was significantly lower than those in plasma, while there was a lack of a proportional relationship with the abundance of plasma SHs. Dehydroepiandrosterone sulfate, the most abundant plasma SH, was undetectable in AH, implying that the blood–aqueous barrier might specifically block its transferral. Axial myopia generally results from many factors throughout the entire eye from tissues and molecules; furthermore, the correlation of AH SHs and axial myopia was assessed to look for their indication in such myopia. The panel with five kinds of AH SHs (F, COR, CORT, ALD and A2) was functional as a discriminator for axial myopia and control. The abundance of SHs, therefore, has a specific distribution in AH and can potentially contribute to axial myopia.

In deep learning, the vanilla stochastic gradient descent (SGD) and SGD with heavy-ball momentum (SGDM) methods have a wide range of applications due to their simplicity and great generalization. This paper uses an exponential scaling method to realize a smooth and stable transition from SGDM to SGD, which combines the advantages of the fast training speed of SGDM and the accurate convergence of SGD (named TSGD). We also provide some theoretical results on the convergence of this algorithm. At the same time, we take advantage of the learning rate warmup strategy’s stability and the learning rate decay strategy’s high accuracy. A warmup–decay learning rate strategy with double exponential functions is proposed (named 2ExpLR). The experimental results on different datasets for the proposed algorithms indicate that the accuracy is improved significantly and that the training is faster and more stable.

As protection for nuclear power plants is quite necessary, the nuclear fuel is sealed in zirconium alloy thin wall cladding. During service, fuel rods might be damaged caused by wall-thickness thinning, cladding corrosion and cracking, etc. This will cause the coolant to enter into the fuel rod, which may lead to the failure of the fuel assembly. However, current diagnostic methods have limitations due to the special structure of the fuel assembly and the underwater and radioactive environment. In this paper, a novel inspection method is proposed to recognize the failure of a fuel rod. The fuel rod failure can be detected based on the presence or absence of coolant inside the fuel rod by using an ultrasonic plate wave. The inspection model and process algorithm are proposed for in-service inspection. The relationship between signal and scanning position is established and analyzed. Both ultrasound field simulation and experiment have been carried out for validation. The corresponding results illustrate that the failed nuclear fuel rod of the whole fuel assembly (including the internal rods) can be effectively detected without the influence of the near-field region by using the proposed method.

We present a novel algorithm (DAM) for deterministic motion trajectory segmentation by using epipolar geometry and adaptive kernel-scale voting. DAM is based on geometric models and exploits the information derived from superpixels to deterministically construct a set of initial correlation matrices. Then DAM introduces a novel adaptive kernel-scale voting scheme to measure each initial correlation matrix. After that, based on the voting scores, the set of initial correlation matrices is accumulated to generate a discriminant affinity matrix, which is utilized for final grouping. The key characteristic of the DAM is its deterministic nature, i.e., DAM is able to achieve reliable and consistent performance for motion trajectory segmentation without randomness. Experimental results on both several traditional datasets (i.e., Hopkins155, Hopkins12, and MTPV62 datasets) and a more realistic and challenging dataset (i.e., KT3DMoSeg) show the significant superiority of the proposed DAM over several state-of-the-art motion trajectory segmentation algorithms with respect to segmentation accuracy.

Convolutional neural network (CNN) has achieved great success in the compressed sensing-based magnetic resonance imaging (CS-MRI). Latest deep networks for CS-MRI usually consist of a stack of sub-networks, each of which refines the former image prediction to a more accurate one. However, as the sub-network number increases, the information in prior sub-networks has a little influence on subsequent ones, which increases the training difficulties and limits the reconstruction performance of the deep model. In this paper, we propose a novel network, named very deep densely connected network (VDDCN), for CS-MRI. Dense connections are introduced to connect any two sub-networks of VDDCN, so each sub-network can make full use of all former predictions, boosting the reconstruction performance of the whole network. The sub-network of VDDCN is composed of feature extraction and fusion block (FEFB) processing data in the image domain and data consistency (DC) layer enforcing the data fidelity in k-space. Specifically, in FEFB, multi-level features are extracted by the recursive feature extraction and fusion sub-blocks (RFEFSBs) and fused locally to obtain the compact features. The VDDCN is much deeper than the prior deep learning models and able to discover more MR image details. The experimental results demonstrate that our proposed VDDCN outperforms other state-of-the-art CS-MRI methods.

Digital pathology and microscopic image analysis play an important role in cell morphology research. In particular, the effective segmentation of White Blood Cells (WBCs) remains a challenging problem due to the blurring boundaries of WBCs under rapid staining, as well as the adhesion between leukocytes and other cells. In this paper, we propose a novel WBC (including nuclei and cells) segmentation algorithm based on both sparsity and geometry constraints. Specifically, we first construct a sparse image representation via combining the HSL color space and the RGB color channels, followed by the use of a sparsity constraint to only preserve useful information from the nuclei features. In addition, we introduce a robust model fitting strategy (i.e., the geometry constraint) to detect cells. Our model fitting strategy is able to significantly improve the robustness of the proposed segmentation algorithm against outliers that could seriously contaminate WBCs. The experimental results show that the proposed algorithm presents clear advantages over the state-of-the-art WBC segmentation algorithms in terms of accuracy.

Pterygium is a common ocular surface disease characterized by abnormal fibrovascular proliferation and invasion, similar to tumorigenesis. The formation of tumors is related to a change in the expression of various RNAs; however, whether they are involved in the formation and development of pterygium remains unclear. In this study, transcriptome analysis of messenger RNAs (mRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs) of paired pterygium and normal conjunctiva was performed to explore key genes regulating the development of pterygium. In total, 579 mRNAs, 275 lncRNAs, and 21 circRNAs were differentially expressed (DE) in pterygium compared with paired conjunctival tissues. Functional enrichment analysis indicated that DE RNAs were associated with extracellular matrix organization, blood vessel morphogenesis, and focal adhesion. Furthermore, through protein-protein interaction network and mRNA-lncRNA co-expression network analysis, key mRNAs including FN1, VCAM1, and MMP2, and key lncRNAs including MIR4435-2HG and LINC00968 were screened and might be involved in the pathogenesis of pterygium. In addition, several circRNAs including hsa_circ_0007482 and hsa_circ_001730 were considered to be involved in the pterygium development. This study provides a scientific basis for elucidating the pathogenesis of pterygium and will be beneficial for the development of preventive and therapeutic strategies.

Cytotoxic T-lymphocyte associated protein 4 (CTLA-4) and programmed cell death 1 (PD-1) are well-known key immune checkpoints that play a crucial dampening effect on regulating T-cell homeostasis and self-tolerance. In this study, we aimed to evaluate the association between immune checkpoints (CTLA-4 and PD-1) and Posner-Schlossman syndrome (PSS) in a southern Chinese population. A total of 137 patients with PSS and 139 healthy controls from a southern Chinese population were recruited. Five single nucleotide polymorphisms (SNPs) of CTLA-4 (rs733618, rs4553808, rs5742909, rs231775, and rs3087243) and five SNPs of PD-1 (rs10204525, rs2227981, rs2227982, rs41386349, and rs36084323) were genotyped by SNaPshot technique. Soluble CTLA-4 (sCTLA-4) and soluble PD-1 (sPD-1) were determined by ELISA and antibody array assay, respectively. The frequencies of T allele at rs733618 and A allele at rs231775 of CTLA-4 were significantly higher in PSS patients than in healthy controls (corrected p (Pc) = 0.037; Pc = 0.044, respectively). The haplotype frequencies of CACGG haplotype (rs733618-rs4553808-rs5742909-rs231775-rs3087243) of CTLA-4 and TGAGC haplotype (rs10204525-rs2227981-rs2227982-rs41386349-rs36084323) of PD-1 in the PSS group was significantly lower than those in the control group (Pc = 0.015, p = 0.034, respectively). Circulating plasma levels of sCTLA-4 and sPD-1 in PSS patients were significantly higher than those in controls (all p < 0.001). The present study suggests that CTLA-4 and PD-1 genetic polymorphisms are associated with the susceptibility to PSS in a southern Chinese population. The upregulated circulating plasma protein levels of sCTLA-4 and sPD-1 might provide some hints regarding the dysfunction of immune checkpoints in PSS during the active status.

Deep insights into microstructures and domain wall behaviors in the evaluation of different material statuses under elastic and plastic stress ranges have essential implications for magnetic sensing and nondestructive testing and evaluation (NDT&E). This paper investigates the repeatability and stability of residual magnetic field (RMF) signals using a magneto-optical Kerr effect microscope for the stress characterization of silicon steel sheets beyond their elastic limit. Real-time domain motion is used for RMF characterization, while both the repeatability under plastic ranges after the cyclic stress rounds and stability during relaxation time are studied in detail. The distinction between elastic and plastic materials is discussed in terms of their spatio-temporal properties for further residual stress measurement since both ranges are mixed. During the relaxation time, the RMF of the plastic material shows a two-stage change with apparent recovery, which is contrasted with the one-stage change in the elastic material. Results show that the grain boundary affects the temporal recovery of the RMF. These findings concerning the spatio-temporal properties of different RMFs in plastic and elastic materials can be applied to the design and development of magnetic NDT&E for (residual) stress measurement and material status estimation.

A novel ratiometric fluorescent immunoassay was developed based on silver nanoparticles (AgNPs) for the sensitive determination of dibutyl phthalate (DBP). In the detection system, AgNPs were labeled on the secondary antibody (AgNPs@Ab2) for signal amplification, which aimed to regulate the H2O2 concentrations. When AgNPs-Ab2 and antigen–primary antibody (Ab1) were linked by specific recognition, the blue fluorescence of Scopoletin (SC) could be effectively quenched by the H2O2 added while the red fluorescence of Amplex Red (AR) was generated. Under the optimized conditions, the calculated detection of limit (LOD, 90% inhibition) reached 0.86 ng/mL with a wide linear range of 2.31–66.84 ng/mL, which was approximately eleven times lower than that by HRP-based traditional ELISA with the same antibody. Meanwhile, it could improve the inherent built-in rectification to the environment by the combination of the dual-output ratiometric fluorescence assays with ELISA, which also enhanced the accuracy and precision (recoveries, 87.20–106.62%; CV, 2.57–6.54%), indicating it can be applied to investigate the concentration of DBP in water samples.

In magnetic resonance imaging (MRI), the super-resolution technology has played a great role in improving image quality. The aim of this paper is to improve edges of brain MRI by incorporating the gradient information of another contrast high-resolution image. Multi-contrast images are assumed to possess the same gradient direction in a local pattern. We proposed to establish a relation model of gradient value between different contrast images to restore a high-resolution image from its input low-resolution version. The similarity of image patches is employed to estimate intensity parameters, leading a more accurate reconstructed image. Then, an iterative back-projection filter is applied to the reconstructed image to further increase the image quality. The new approach is verified on synthetic and real brain MRI images and achieves higher visual quality and higher objective quality criteria than the compared state-of-the-art super-resolution approaches. The gradient information of the multi-contrast MRI images is very useful. With a proper relation model, the proposed method enhances image edges in MRI image super-resolution. Improving the MRI image resolution from very low-resolution observations is challenging. We tackle this problem by first modeling the relation of gradient value in multi-contrast MRI and then performing fast supper-resolution methods. This relation model may be helpful for other MRI reconstruction problems.

Stress affects the microstructure of the material to influence the durability and service life of the components. However, the previous work of stress measurement lacks quantification of the different variations in time and spatial features of micromagnetic properties affected by stress in elastic and plastic ranges, as well as the evolution of microstructure. In this paper, microstructure evolution under stress in elastic and plastic ranges is evaluated by magnetic Barkhausen noise (MBN) transient analysis. Based on a J-A model, the duration and the intensity are the eigenvalues for MBN transient analysis to quantify transient size and number of Barkhausen events under stress. With the observation of domain wall (DW) distribution and microstructure, the correlation between material microstructure and MBN transient eigenvalues is investigated to verify the ability of material status evaluation on the microscopic scale of the method. The results show that the duration and the intensity have different change trends in elastic and plastic ranges. The eigenvalue fusion of the duration and intensity distinguishes the change in microstructure under the stress in elastic and plastic deformation. The appearance of grain boundary (GB) migration and dislocation under the stress in the plastic range makes the duration and the intensity higher on the GB than those inside the grain. Besides, the reproducibility of the proposed method is investigated by evaluating microstructure evolution for silicon steel sheet and Q235 steel sheet. The proposed method investigates the correlation between the microstructure and transient micromagnetic properties, which has the potential for stress evaluation in elastic and plastic ranges for industrial materials.

Pipelines play an important role in the national/international transportation of natural gas, petroleum products, and other energy resources. Pipelines are set up in different environments and consequently suffer various damage challenges, such as environmental electrochemical reaction, welding defects, and external force damage, etc. Defects like metal loss, pitting, and cracks destroy the pipeline’s integrity and cause serious safety issues. This should be prevented before it occurs to ensure the safe operation of the pipeline. In recent years, different non-destructive testing (NDT) methods have been developed for in-line pipeline inspection. These are magnetic flux leakage (MFL) testing, ultrasonic testing (UT), electromagnetic acoustic technology (EMAT), eddy current testing (EC). Single modality or different kinds of integrated NDT system named Pipeline Inspection Gauge (PIG) or un-piggable robotic inspection systems have been developed. Moreover, data management in conjunction with historic data for condition-based pipeline maintenance becomes important as well. In this study, various inspection methods in association with non-destructive testing are investigated. The state of the art of PIGs, un-piggable robots, as well as instrumental applications, are systematically compared. Furthermore, data models and management are utilized for defect quantification, classification, failure prediction and maintenance. Finally, the challenges, problems, and development trends of pipeline inspection as well as data management are derived and discussed.

There is a need in industry to supply safe, effective and reliable technique to characterize the stress of steel components and structures, both at the manufacturing stage and in service. Bridging the correlation between micro and macro magnetic properties and the applied tensile stress is the first conceptual step to come up with a new method of non-destructive material testing. We investigate the stress-associated changes in domain wall dynamics in grain-oriented electrical steel by in-situ magnetic imaging using magneto-optical indicator films. The 180° domain walls velocity distribution is used as a parameter for applied stress determination. Additionally, the in-plane magnetic stray field above the surface of the sample is synchronously measured for stress evaluation. The variations in magnetic stray field outside the sample under different loading are investigated for the analysis of the domain wall dynamics. From this, an interrelation of the domain wall dynamics and magnetic stray fields with varied tensile stress is derived. The results provide substantial microscopic and macroscopic insight for the interplay of domain wall dynamics and stress-induced demagnetizing effect.

Stress is the crucial factor of ferromagnetic material failure origin. However, the nondestructive test methods to analyze the ferromagnetic material properties’ inhomogeneity on the microscopic scale with stress have not been obtained so far. In this study, magnetic Barkhausen noise (MBN) signals on different silicon steel sheet locations under in situ tensile tests were detected by a high-spatial-resolution magnetic probe. The domain-wall (DW) motion, grain, and grain boundary were detected using a magneto-optical Kerr (MOKE) image. The time characteristic of DW motion and MBN signals on different locations was varied during elastic deformation. Therefore, a time-response histogram is proposed in this work to show different DW motions inside the grain and around the grain boundary under low tensile stress. In order to separate the variation of magnetic properties affected by the grain and grain boundary under low tensile stress corresponding to MBN excitation, time-division was carried out to extract the root-mean-square (RMS), mean, and peak in the optimized time interval. The time-response histogram of MBN evaluated the silicon steel sheet’s inhomogeneous material properties, and provided a theoretical and experimental reference for ferromagnetic material properties under stress.

Ultrasound microbubble combined optic protection drugs have obvious protective effect on optic nerve damage. This way of targeting drug delivery is becoming more simple, not through the whole body metabolism, avoiding drug via blood circulation when facing the decomposition and the environment in the interference and destruction process of drugs, to maximize the guarantee to reach target organs of drug concentration and to reache the maximum therapeutic effect. The technique of ultrasound microbubbles is safe, controllable, nonimmunogenic, and repeatable. It provides us with a novel idea in the administration of neuroprotective drugs.

AIM: To observe the pathological changes of the lower segment of nasolacrimal duct mucosa in rabbits at different stages after retrograde lachrymal dilated drainage tube implantation. METHODS: Totally 14 New Zealand rabbits were used in the present study. One side of nasolacrimal duct was obstructed to produce an experimental model and operated the reverse implantation of nasolacrimal duct intubation. Histological changes of the lower segment of nasolacrimal duct mucosa were observed by routine light microscope at 2, 4, 6, 8, 10, 12 and 14wk after the operation. RESULTS: Compared with the control side, the group of 2 and 4wk after surgery presented the inflammatory cytokine. The group of 12wk after the operation presented isolated granuloma. Group 12 and 14wk presented scattered granuloma. The size of the granulomas was smaller and the density of epithelioid cell and fibroblast were lower in group 12wk than those in group 14wk by HE and Masson trichrome stain.CONCLUSION: Recurrent Silicone Tube is used to treat nasolacrymal duct obstruction. Nasolacrimal duct can be narrowed and blocked again by granuloma, progressive fibrosis and adhesion of surrounding tissues when tube is in the duct more than 12wk.

AIM: To evaluate the features and clinical outcomes of cataract extraction by phacoemulsification with intraocular lens implantation in primary angle-closure suspect(PACS), primary angle-closure(PAC)and primary angle-closure glaucoma(PACG)with cataract.METHODS:Phacoemulsification with intraocular lens implantation was performed on 86 cases(86 eyes)diagnosed as PACS, PAC and PACG co-existing cataract from January to December 2012. All cases were followed up for 3 months to 1 year. Pre-operative and post-operative visual acuity, intraocular pressure(IOP), gonioscopy, ultrasound biomicroscopy(UBM), visual field and usage of anti-glaucomaous eye drops were recorded.RESULTS:Zonular dialysis existed in 19 eyes(22%). The post-operative visual acuity improved in 84 eyes(98%). The post-operative visual acuity was CONCLUSION: PACS, PAC and PACG co-existing zonular dialysis is common. Phacoemulsification with IOL implantation can reduce IOP, deepen anterior chamber and open angle.